19 research outputs found
Investigating Brain Functional Networks in a Riemannian Framework
The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain.
The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects.
In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them.
Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices.
Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning
The legibility of the imaged human brain
Our knowledge of the organisation of the human brain at the population-level
is yet to translate into power to predict functional differences at the
individual-level, limiting clinical applications, and casting doubt on the
generalisability of inferred mechanisms. It remains unknown whether the
difficulty arises from the absence of individuating biological patterns within
the brain, or from limited power to access them with the models and compute at
our disposal. Here we comprehensively investigate the resolvability of such
patterns with data and compute at unprecedented scale. Across 23810 unique
participants from UK Biobank, we systematically evaluate the predictability of
25 individual biological characteristics, from all available combinations of
structural and functional neuroimaging data. Over 4526 GPU*hours of
computation, we train, optimize, and evaluate out-of-sample 700 individual
predictive models, including multilayer perceptrons of demographic,
psychological, serological, chronic morbidity, and functional connectivity
characteristics, and both uni- and multi-modal 3D convolutional neural network
models of macro- and micro-structural brain imaging. We find a marked
discrepancy between the high predictability of sex (balanced accuracy 99.7%),
age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute
error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance,
and the surprisingly low predictability of other characteristics. Neither
structural nor functional imaging predicted individual psychology better than
the coincidence of common chronic morbidity (p<0.05). Serology predicted common
morbidity (p<0.05) and was best predicted by it (p<0.001), followed by
structural neuroimaging (p<0.05). Our findings suggest either more informative
imaging or more powerful models will be needed to decipher individual level
characteristics from the brain.Comment: 36 pages, 6 figures, 1 table, 2 supplementary figure
The effect of using multiple connectivity metrics in brain Functional Connectivity studies
Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2022Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to assist as a
diagnostic or prognostic tool for a diverse set of neurological and neuropsychiatric disorders, which are
often difficult to differentiate. fMRI focuses on the study of the brain functional Connectome, which is
characterized by the functional connections and neuronal activity among different brain regions, also
interpreted as communications between pairs of regions. This Functional Connectivity (FC) is quantified
through the statistical dependences between brain regions’ blood-oxygen-level-dependent (BOLD)
signals time-series, being traditionally evaluated by correlation coefficient metrics and represented as
FC matrices. However, several studies underlined limitations regarding the use of correlation metrics to
fully capture information from these signals, leading investigators towards different statistical metrics
that would fill those shortcomings. Recently, investigators have turned their attention to Deep Learning
(DL) models, outperforming traditional Machine Learning (ML) techniques due to their ability to
automatically extract relevant information from high-dimensional data, like FC data, using these models
with rs-fMRI data to improve diagnostic predictions, as well as to understand pathological patterns in
functional Connectome, that can lead to the discovery of new biomarkers. In spite of very encouraging
performances, the black-box nature of DL algorithms makes difficult to know which input information
led the model to a certain prediction, restricting its use in clinical settings.
The objective of this dissertation is to exploit the power of DL models, understanding how FC
matrices created from different statistical metrics can provide information about the brain FC, beyond
the conventionally used correlation family. Two publicly available datasets where studied, the ABIDE I dataset, composed by healthy and autism spectrum disease (ASD) individuals, and the ADHD-200
dataset, with typically developed controls and individuals with attention-deficit/hyperactive disorder
(ADHD). The computation of the FC matrices of both datasets, using different statistical metrics, was
performed in MATLAB using MULAN’s toolbox functions, encompassing the correlation coefficient,
non-linear correlation coefficient, mutual information, coherence and transfer entropy. The
classification of FC data was performed using two DL models, the improved ConnectomeCNN model
and the innovative ConnectomeCNN-Autoencoder model. Moreover, another goal is to study the effect
of a multi-metric approach in classification performances, combining multiple FC matrices computed
from the different statistical metrics used, as well as to study the use of Explainable Artificial
Intelligence (XAI) techniques, namely Layer-wise Relevance Propagation method (LRP), to surpass the
black-box problem of DL models used, in order to reveal the most important brain regions in ADHD.
The results show that the use of other statistical metrics to compute FC matrices can be a useful
complement to the traditional correlation metric methods for the classification between healthy subjects
and subjects diagnosed with ADHD and ASD. Namely, non-linear metrics like h2 and mutual
information, achieved similar and, in some cases, even slightly better performances than correlation
methods. The use of FC multi-metric, despite not showing improvements in classification performance
compared to the best individual method, presented promising results, namely the ability of this approach
to select the best features from all the FC matrices combined, achieving a similar performance in relation
to the best individual metric in each of the evaluation measures of the model, leading to a more complete
classification. The LRP analysis applied to ADHD-200 dataset proved to be promising, identifying brain
regions related to the pathophysiology of ADHD, which are in broad accordance with FC and structural
study’s findings.A ressonância magnética funcional em estado de repouso (rs-fMRI) tem o potencial de ser uma
ferramenta auxiliar de diagnóstico ou prognóstico para um conjunto diversificado de distúrbios
neurológicos e neuropsiquiátricos, que muitas vezes são difíceis de diferenciar. A análise de dados de
rs-fMRI recorre muitas vezes ao conceito de conectoma funcional do cérebro, que se caracteriza pelas
conexões funcionais entre as diferentes regiões do cérebro, sendo estas conexões interpretadas como
comunicações entre diferentes pares de regiões cerebrais. Esta conectividade funcional é quantificada
através de dependências estatísticas entre os sinais fMRI das regiões cerebrais, sendo estas
tradicionalmente calculadas através da métrica coeficiente de correlação, e representadas através de
matrizes de conectividade funcional. No entanto, vários estudos demonstraram limitações em relação ao
uso de métricas de correlação, em que estas não conseguem capturar por completo todas as informações
presentes nesses sinais, levando os investigadores à procura de diferentes métricas estatísticas que
pudessem preencher essas lacunas na obtenção de informações mais completas desses sinais.
O estudo destes distúrbios neurológicos e neuropsiquiátricos começou por se basear em técnicas
como mapeamento paramétrico estatístico, no contexto de estudos de fMRI baseados em tarefas. Porém,
essas técnicas apresentam certas limitações, nomeadamente a suposição de que cada região cerebral atua
de forma independente, o que não corresponde ao conhecimento atual sobre o funcionamento do cérebro.
O surgimento da rs-fMRI permitiu obter uma perspetiva mais global e deu origem a uma vasta literatura
sobre o efeito de patologias nos padrões de conetividade em repouso, incluindo tentativas de diagnóstico
automatizado com base em biomarcadores extraídos dos conectomas. Nos últimos anos, os
investigadores voltaram a sua atenção para técnicas de diferentes ramos de Inteligência Artificial, mais
propriamente para os algoritmos de Deep Learning (DL), uma vez que são capazes de superar os
algoritmos tradicionais de Machine Learning (ML), que foram aplicados a estes estudos numa fase
inicial, devido à sua capacidade de extrair automaticamente informações relevantes de dados de alta
dimensão, como é o caso dos dados de conectividade funcional. Esses modelos utilizam os dados obtidos
da rs-fMRI para melhorar as previsões de diagnóstico em relação às técnicas usadas atualmente em
termos de precisão e rapidez, bem como para compreender melhor os padrões patológicos nas conexões
funcionais destes distúrbios, podendo levar à descoberta de novos biomarcadores. Apesar do notável
desempenho destes modelos, a arquitetura natural em caixa-preta dos algoritmos de DL, torna difícil
saber quais as informações dos dados de entrada que levaram o modelo a executar uma determinada
previsão, podendo este utilizar informações erradas dos dados para alcançar uma dada inferência,
restringindo o seu uso em ambientes clínicos.
O objetivo desta dissertação, desenvolvida no Instituto de Biofísica e Engenharia Biomédica, é
explorar o poder dos modelos DL, de forma a avaliar até que ponto matrizes de conectividade funcional
criadas a partir de diferentes métricas estatísticas podem fornecer mais informações sobre a
conectividade funcional do cérebro, para além das métricas de correlação convencionalmente usadas
neste tipo de estudos. Foram estudados dois conjuntos de dados bastante utilizados em estudos de
Neurociência e que estão disponíveis publicamente: o conjunto de dados ABIDE-I, composto por
indivíduos saudáveis e indivíduos com doenças do espectro do autismo (ASD), e o conjunto de dados
ADHD-200, com controlos tipicamente desenvolvidos e indivíduos com transtorno do défice de atenção
e hiperatividade (ADHD).
Numa primeira fase foi realizada a computação das matrizes de conetividade funcional de ambos os
conjuntos de dados, usando as diferentes métricas estatísticas. Para isso, foi desenvolvido código de
MATLAB, onde se utilizam as séries temporais dos sinais BOLD obtidas dos dois conjuntos de dados
para criar essas mesmas matrizes de conectividade funcional, incorporando funções de diferentes
métricas estatísticas da caixa de ferramentas MULAN, compreendendo o coeficiente de correlação, o
coeficiente de correlação não linear, a informação mútua, a coerência e a entropia de transferência. De
seguida, a classificação dos dados de conectividade funcional, de forma a avaliar o efeito do uso de
diferentes métricas estatísticas para a criação de matrizes de conectividade funcional na discriminação
de sujeitos saudáveis e patológicos, foi realizada usando dois modelos de DL. O modelo
ConnectomeCNN melhorado e o modelo inovador ConnectomeCNN-Autoencoder foram desenvolvidos
com recurso à biblioteca de Redes Neuronais Keras, juntamente com o seu backend Tensorflow, ambos
em Python. Estes modelos, desenvolvidos previamente no Instituto de Biofísica e Engenharia
Biomédica, tiveram de ser otimizados de forma a obter a melhor performance, onde vários parâmetros
dos modelos e do respetivo treino dos mesmos foram testados para os dados a estudar. Pretendeu-se
também estudar o efeito de uma abordagem multi-métrica nas tarefas de classificação dos sujeitos de
ambos os conjuntos de dados, sendo que, para estudar essa abordagem as diferentes matrizes calculadas
a partir das diferentes métricas estatísticas utilizadas, foram combinadas, sendo usados os mesmos
modelos que foram aplicados às matrizes de conectividade funcional de cada métrica estatística
individualmente. É importante realçar que na abordagem multi-métrica também foi realizada a
otimização dos parâmetros dos modelos utilizados e do respetivo treino, de modo a conseguir a melhor
performance dos mesmos para estes dados. Para além destes dois objetivos, estudou-se o uso de técnicas
de Inteligência Artificial Explicável (XAI), mais especificamente o método Layer-wise Relevance
Propagation (LRP), com vista a superar o problema da caixa-preta dos modelos de DL, com a finalidade
de explicar como é que os modelos estão a utilizar os dados de entrada para realizar uma dada previsão.
O método LRP foi aplicado aos dois modelos utilizados anteriormente, usando como dados de entrada
o conjunto de dados ADHD-200, permitindo assim revelar quais as regiões cerebrais mais importantes
no que toca a um diagnóstico relacionado com o ADHD.
Os resultados obtidos mostram que o uso de outras métricas estatísticas para criar as matrizes de
Conectividade Funcional podem ser um complemento bastante útil às métricas estatísticas
tradicionalmente utilizadas para a classificação entre indivíduos saudáveis e indivíduos como ASD e
ADHD. Nomeadamente métricas estatísticas não lineares como o h2 e a informação mútua, obtiveram
desempenhos semelhantes e, em alguns casos, desempenhos ligeiramente melhores em relação aos
desempenhos obtidos por métodos de correlação, convencionalmente usados nestes estudos de
conectividade funcional. A utilização da multi-métrica de conectividade funcional, apesar de não
apresentar melhorias no desempenho geral da classificação em relação ao melhor método das matrizes
de conectividade funcional individuais do conjunto de métricas estatísticas abordadas, apresenta
resultados que justificam a exploração mais aprofundada deste tipo de abordagem, de forma a
compreender melhor a complementaridade das métricas e a melhor maneira de as utilizar. O uso do
método LRP aplicado ao conjunto de dados do ADHD-200 mostrou a sua aplicabilidade a este tipo de
estudos e a modelos de DL, identificando as regiões cerebrais mais relacionadas à fisiopatologia do
diagnóstico do ADHD que são compatíveis com o que é reportado por diversos estudos de conectividade
funcional e estudos de alterações estruturais associados a esta doença. O facto destas técnicas de XAI
demonstrarem como é que os modelos de DL estão a usar os dados de entrada para efetuar as previsões,
pode significar uma mais rápida e aceite adoção destes algoritmos em ambientes clínicos. Estas técnicas
podem auxiliar o diagnóstico e prognóstico destes distúrbios neurológicos e neuropsiquiátricos, que são
na maioria das vezes difíceis de diferenciar, permitindo aos médicos adquirirem um conhecimento em
relação à previsão realizada e poder explicar a mesma aos seus pacientes
Decoding Task-Based fMRI Data Using Graph Neural Networks, Considering Individual Differences
Functional magnetic resonance imaging (fMRI) is a non-invasive technology that provides high spatial resolution in determining the human brain\u27s responses and measures regional brain activity through metabolic changes in blood oxygen consumption associated with neural activity. Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific task performance. Over the past several years, a variety of computational methods have been proposed to decode task fMRI data that can identify brain regions associated with different task stimulations. Despite the advances made by these methods, several limitations exist due to graph representations and graph embeddings transferred from task fMRI signals. In the present study, we proposed an end-to-end graph convolutional network by combining the convolutional neural network with graph representation, with three convolutional layers to classify task fMRI data from the Human Connectome Project (302 participants, 22–35 years of age). One goal of this dissertation was to improve classification performance. We applied four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the brain functional graph, then evaluated the performance of the classification model. The empirical results indicated that the proposed GCN framework accurately identified the brain\u27s state in task fMRI data and achieved comparable macro F1 scores of 0.978 and 0.976 with the NetMF and RandNE embedding methods, respectively. Another goal of the dissertation was to assess the effects of individual differences (i.e., gender and fluid intelligence) on classification performance. We tested the proposed GCN framework on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data
An alternative approach for assessing drug induced seizures, using non-protected larval zebrafish
As many as 9% of epileptic seizures occur as a result of drug toxicity.
Identifying compounds with seizurogenic side effects is imperative for assessing
compound safety during drug development, however, multiple marketed drugs
still have clinical associations with seizures. Moreover, current approaches for
assessing seizurogenicity, namely rodent EEG and behavioural studies, are
highly resource intensive. This being the case, alternative approaches have
been postulated for assessing compound seizurogenicity, including in vitro, in
vivo, and in silico methods.
In this thesis, experimental work is presented supporting the use of larval
zebrafish as a candidate model organism for developing new seizure liability
screening approaches. Larval zebrafish are translucent, meaning they are
highly amenable to imaging approaches while offering a more ethical alternative
to mammalian research. Zebrafish are furthermore highly fecund facilitating
capacity for both high replication and high throughput. The primary goal of this
thesis was to identify biomarkers in larval zebrafish, both behavioural and
physiological, of compounds that increase seizure liability.
The efficacy of this model organism for seizure liability testing was assessed
through exposure of larval zebrafish to a mechanistically diverse array of
compounds, selected for their varying degrees of seizurogenicity. Their central
nervous systems were monitored using a variety of different techniques
including light sheet microscopy, local field potential recordings, and
behavioural monitoring. Data acquired from these measurements were then
analysed using a variety of techniques including frequency domain analysis,
clustering, functional connectivity, regression, and graph theory. Much of this
analysis was exploratory in nature and is reflective of the infancy of the field.
Experimental findings suggest that larval zebrafish are indeed sensitive to a
wide range of pharmacological mechanisms of action and that drug actions are
reflected by behavioural and direct measurements of brain activity. For
example, local field potential recordings revealed electrographic responses akin
to pre-ictal, inter-ictal and ictal events identified in humans. Ca2+ imaging using
light sheet microscopy found global increases in fluorescent intensity and
functional connectivity due to seizurogenic drug administration. In addition,
[2]
further functional connectivity and graph analysis revealed macroscale network
changes correlated with drug seizurogenicity and mechanism of action. Finally,
analysis of swimming behaviour revealed a strong correlation between high speed swimming behaviours and administration of convulsant compounds.
In conclusion, presented herein are data demonstrating the power of functional
brain imaging, LFP recordings, and behavioral monitoring in larval zebrafish for
assessing the action of neuroactive drugs in a highly relevant vertebrate model.
These data help us to understand the relevance of the 4 dpf larval zebrafish for
neuropharmacological studies and reveal that even at this early developmental
stage, these animals are highly responsive to a wide range of neuroactive
compounds across multiple primary mechanisms of action. This represents
compelling evidence of the potential utility of larval zebrafish as a model
organism for seizure liability testing